Data-driven marketing continues to grow by leaps and bounds. It’s on the minds of many marketers, and is in evidence as online advertisers amass search, demographic, behavioral and intent data to design their campaigns.
Each type of data has its place and value. As the volume of data skyrockets, it's useful to step back and consider the different types of data, where they come from and how they can be used most effectively. Unique and highly focused data can be used as a signal booster for more effective intent-based targeting.
Marketers are becoming increasingly aware of the value of their own data -- and of the value of third-party data available from a wide range of sources. Much of this is general behavioral data based on online activities. It can be layered on top of a marketer’s own information to help identify lookalike audiences or for granular audience targeting. Used appropriately, it can be very effective in reaching audiences with online campaigns.
In many industries there is an increasing trend toward a more focused approach to audience data. This is not based simply on a company's own data, nor is it built on general third-party behavioral data. The most valuable data is unique, aggregated from multiple industry sources and highly relevant to the task at hand. It also has the critical ability to demonstrate consumer intent.
The travel industry provides an excellent example of how focused data can be used. Search and purchase data from multiple travel service providers can be combined to create a pool of clear intent. It can show who will be traveling, where they will be traveling, when they will be traveling, and even provide details such as many people are in a party.
Even aggregated and without any personally identifiable information, this is incredibly valuable information for a marketer looking to sell downstream travel services, such as hotel rooms or rental cars. Intent-based targeting and retargeting using focused data improves the value of impressions by limiting them only to people who need a service at the very time they are most likely to be seeking that service. It augments first-party data with highly relevant third-party data to create strong signals for reaching customers in a cluttered media environment.
Let’s look at how this approach might work in practice. It’s well understood that people typically book travel in a fairly set order: airline tickets first, then hotels, and finally rental cars and in-market activities. These steps occur as the travel date draws closer, with rental car bookings often happening only within 72 hours of a trip. Rental car companies are aware of this, but they’re not aware of who will be traveling during any specific time frame. This limits their options.
Rental car companies could simply use search data, targeting anyone who looks for destination information; but this is inefficient and would result in wasted impressions. They could use general behavioral targeting based on visits to various travel-related sites; that could reach people likely to travel, but it wouldn’t provide any insight into timing. The targeting might be improved but impressions would still be wasted.
A more successful approach is a focused data model that would include search and purchase data from multiple travel industry sources. This allows rental car marketers to deliver impressions only to people traveling within a specific time frame. Focused travel data provides actionable intent information that marketers can use as a signal strengthener to direct their efforts most effectively.
Data is everywhere, and new types of data become available every day –- along with new ways to put it to work. Marketers must recognize that all data is not equally suited to every purpose -- and must consider the goals of a program, the audience they are trying to reach and the types of focused data most likely to provide actionable insights into that audience’s intentions. Once they do, they can begin exploring which sources provide the information that will be the most effective for them and their campaigns.